Project: No-show appointments Data Analysis

Table of Contents

• Introduction
• Data Wrangling
• Exploratory Data Analysis
• Conclusions

Introduction

In this project, the data set we have selected for analysis is the No-show appointments data set. This data set is a collection of the medical appointments from Brazil in the year 2016. Our main focus is to determine whether or not patients show up for appointments and find out what factors are important to know in order to predict if a patient will show up for appointment or not. The dataset consists of $14$ columns which tells us about the characteristics of the patients. Some of which include the following

• Scheduled day : this informs us on what day the patients set up their appointment.
• Neighborhood : specifies the location of the hospital
• Scholarship : tells as whether or not a particular patient is enrolled in the Brasillian welfare program Bolsa Familia.
• No-show : which indicates 'No' if the patient showed up to their appointments and 'Yes' if they did not show up.
• SMS_receved : This tells us whether the patient received an sms notification reminding them about the appointment.
• Gender : Whether the patient is male or female.
• Age : Tells us the age of the patient.

The following are the questions of consideration in our exploration.

Questions

1. Is there any relationship between a patients gender and no-show ?
2. Do patients on Scholarship show up more than patients not on Scholarship?
3. Is there any relationship between a patients age and no-show ?
4. Does getting an SMS reminder have any impact on show up rate?
5. Which neighbourhood has the most number of patients who don't show up for appointments?
6. Is there any relationship between the patient's condition or disease and no-show?

We begin by first importing all the necessary packages that we will require for our analysis. Upon loading our data set, we will begin by first assessing and buliding initution about the data set. To do this, we will check the shape of our data set, the data types of the columns and a concise summary of the dataframe using df.info() as well as the descriptive statistics for each column of the dataframe. After assessing the data, we will then proceed to cleaning our data by checking for missing values, duplicates and also making a few changes on the column names to maintain consistency. We will then explore the data with visuals to find patterns by creating histograms, bar charts and pie charts. Finally, we will then draw conclusions based on descriptive statistics and visualizations.

# imorting all the necessary packages 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import calendar
%matplotlib inline

Data Wrangling

General Properties

# Loading no_show appointments dataset
df=pd.read_csv('noshowappointments-kagglev2-may-2016.csv')

# checking the first 5 rows of the dataset
df.head()

	PatientId	AppointmentID	Gender	ScheduledDay	AppointmentDay	Age	Neighbourhood	Scholarship	Hipertension	Diabetes	Alcoholism	Handcap	SMS_received	No-show
0	2.987250e+13	5642903	F	2016-04-29T18:38:08Z	2016-04-29T00:00:00Z	62	JARDIM DA PENHA	0	1	0	0	0	0	No
1	5.589978e+14	5642503	M	2016-04-29T16:08:27Z	2016-04-29T00:00:00Z	56	JARDIM DA PENHA	0	0	0	0	0	0	No
2	4.262962e+12	5642549	F	2016-04-29T16:19:04Z	2016-04-29T00:00:00Z	62	MATA DA PRAIA	0	0	0	0	0	0	No
3	8.679512e+11	5642828	F	2016-04-29T17:29:31Z	2016-04-29T00:00:00Z	8	PONTAL DE CAMBURI	0	0	0	0	0	0	No
4	8.841186e+12	5642494	F	2016-04-29T16:07:23Z	2016-04-29T00:00:00Z	56	JARDIM DA PENHA	0	1	1	0	0	0	No

# checking the shape of our data set.
df.shape

(110527, 14)

The sample size for our data set is $ 110,527$ and it consists of $14$ columns.

# checking the data types of the dataset.
df.dtypes

PatientId         float64
AppointmentID       int64
Gender             object
ScheduledDay       object
AppointmentDay     object
Age                 int64
Neighbourhood      object
Scholarship         int64
Hipertension        int64
Diabetes            int64
Alcoholism          int64
Handcap             int64
SMS_received        int64
No-show            object
dtype: object

# checking the number of unique values in each column.
df.nunique()

PatientId          62299
AppointmentID     110527
Gender                 2
ScheduledDay      103549
AppointmentDay        27
Age                  104
Neighbourhood         81
Scholarship            2
Hipertension           2
Diabetes               2
Alcoholism             2
Handcap                5
SMS_received           2
No-show                2
dtype: int64

# Checking some general information about our dataset.
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 110527 entries, 0 to 110526
Data columns (total 14 columns):
 #   Column          Non-Null Count   Dtype  
---  ------          --------------   -----  
 0   PatientId       110527 non-null  float64
 1   AppointmentID   110527 non-null  int64  
 2   Gender          110527 non-null  object 
 3   ScheduledDay    110527 non-null  object 
 4   AppointmentDay  110527 non-null  object 
 5   Age             110527 non-null  int64  
 6   Neighbourhood   110527 non-null  object 
 7   Scholarship     110527 non-null  int64  
 8   Hipertension    110527 non-null  int64  
 9   Diabetes        110527 non-null  int64  
 10  Alcoholism      110527 non-null  int64  
 11  Handcap         110527 non-null  int64  
 12  SMS_received    110527 non-null  int64  
 13  No-show         110527 non-null  object 
dtypes: float64(1), int64(8), object(5)
memory usage: 11.8+ MB

From the above information, we see that there are no null values present in our data set.

# checking the distribution of the different characteristic features of our data set 
df.hist(figsize=(10,10));

Data Cleaning

Checking for duplicates and null values

# checking if the dataset consists of duplicates.
sum(df.duplicated())

0

From the above results from the code, we see that the dataset contains no duplicate values. We now check if at all there are any missing values.

# checking for null values
df.isnull().sum()

PatientId         0
AppointmentID     0
Gender            0
ScheduledDay      0
AppointmentDay    0
Age               0
Neighbourhood     0
Scholarship       0
Hipertension      0
Diabetes          0
Alcoholism        0
Handcap           0
SMS_received      0
No-show           0
dtype: int64

The above result confirms that there are no missing or null values in this dataset

# checking the summary statistics of our dataset
df.describe()

	PatientId	AppointmentID	Age	Scholarship	Hipertension	Diabetes	Alcoholism	Handcap	SMS_received
count	1.105270e+05	1.105270e+05	110527.000000	110527.000000	110527.000000	110527.000000	110527.000000	110527.000000	110527.000000
mean	1.474963e+14	5.675305e+06	37.088874	0.098266	0.197246	0.071865	0.030400	0.022248	0.321026
std	2.560949e+14	7.129575e+04	23.110205	0.297675	0.397921	0.258265	0.171686	0.161543	0.466873
min	3.921784e+04	5.030230e+06	-1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	4.172614e+12	5.640286e+06	18.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	3.173184e+13	5.680573e+06	37.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	9.439172e+13	5.725524e+06	55.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
max	9.999816e+14	5.790484e+06	115.000000	1.000000	1.000000	1.000000	1.000000	4.000000	1.000000

From the summary statistics in the above code, we observe that the minimum value for age is negative. This could be as a result of a mismatch which might have occured during data collection. We can thus remove all rows which have age as a negative value. We will also drop the rows with age $0$ as it is not logical to have age as zero.

# dropping the rows which contains age as a negative value as well as 0 value.
df.drop(df.query('Age <= 0 ').index,inplace=True)

# checking if the changes made to age
df.Age.describe()

count    106987.000000
mean         38.316085
std          22.466214
min           1.000000
25%          19.000000
50%          38.000000
75%          56.000000
max         115.000000
Name: Age, dtype: float64

We now have dropped the negative value for age as well as all rows containing age as $0$. We now see that the minimum age is no longer negative.

We now can also drop the columns which will not be of much help in our analysis.

# checking the unique values for Handcap
df['Handcap'].unique()

array([0, 1, 2, 3, 4], dtype=int64)

The Handcap column consists of numerical values 1, 2, 3 and 4 as it is difficult to encode what these numbers means or signify, we shall drop this column.

# drop columns that are extraneous from the dataset 
df.drop(['PatientId', 'AppointmentID', 'Handcap'], axis=1, inplace=True)

# confirm changes
df.head(1)

	Gender	ScheduledDay	AppointmentDay	Age	Neighbourhood	Scholarship	Hipertension	Diabetes	Alcoholism	SMS_received	No-show
0	F	2016-04-29T18:38:08Z	2016-04-29T00:00:00Z	62	JARDIM DA PENHA	0	1	0	0	0	No

To maintain consistency in the column names, we will make a few changes to make our column names more tidy as well as correct an error in the spelling for Hipertension to Hypertension. The following are the changes which we are going to make.

• Putting an underscore on ScheduledDay and AppointmentDay and changing Day to a lower case
• Changing No-show to No_show in the last column.

# Renaming the columns
df.rename(columns={'ScheduledDay':'Scheduled_day', 'AppointmentDay':'Appointment_day','Hipertension':'Hypertension','No-show':'No_show'},inplace=True)
#checking/ confirming changes in the column names
df.head(1)

	Gender	Scheduled_day	Appointment_day	Age	Neighbourhood	Scholarship	Hypertension	Diabetes	Alcoholism	SMS_received	No_show
0	F	2016-04-29T18:38:08Z	2016-04-29T00:00:00Z	62	JARDIM DA PENHA	0	1	0	0	0	No

We now properly encode the data in the following columns: Scholarship, Hypertension, Diabetes, Alcoholism, SMS_received and No_show. We do this so that we can make it more easier to analyse and interpret the meaning of the information in these columns. The following are the changes which we are going to make.

• For the columns Scholarship, Hypertension, Diabetes ,Alcoholism and SMS_received, $0$ will mean "No" and $1$ will mean "Yes", for example "Yes" for SMS_received would mean that a particular patient received an sms reminding them about their scheduled appointment and "No" would mean that they did not receive an sms. For the No_show column, "No" will be replaced with Showed_up for appointment and "Yes" will be No_show_up.

# making the changes to the column values with 0 and 1 to No and yes respectively
df[['Scholarship','Hypertension','Diabetes','Alcoholism','SMS_received']] = df[['Scholarship','Hypertension','Diabetes','Alcoholism','SMS_received']].replace([0,1],['No','Yes'])

# changing the entries in the No_show column
df['No_show'] = df['No_show'].replace(['No','Yes'],['Showed_up','No_show_up'])

# confirming/ checking the changes made
df.head()

	Gender	Scheduled_day	Appointment_day	Age	Neighbourhood	Scholarship	Hypertension	Diabetes	Alcoholism	SMS_received	No_show
0	F	2016-04-29T18:38:08Z	2016-04-29T00:00:00Z	62	JARDIM DA PENHA	No	Yes	No	No	No	Showed_up
1	M	2016-04-29T16:08:27Z	2016-04-29T00:00:00Z	56	JARDIM DA PENHA	No	No	No	No	No	Showed_up
2	F	2016-04-29T16:19:04Z	2016-04-29T00:00:00Z	62	MATA DA PRAIA	No	No	No	No	No	Showed_up
3	F	2016-04-29T17:29:31Z	2016-04-29T00:00:00Z	8	PONTAL DE CAMBURI	No	No	No	No	No	Showed_up
4	F	2016-04-29T16:07:23Z	2016-04-29T00:00:00Z	56	JARDIM DA PENHA	No	Yes	Yes	No	No	Showed_up

# We change the date format for Scheduled_day and Appointment_day to date_time
df['Scheduled_day'] = pd.to_datetime(df['Scheduled_day']) 
df['Scheduled_day'] = df['Scheduled_day'].dt.date 
                                     

df['Appointment_day'] = pd.to_datetime(df['Appointment_day']) 
df['Appointment_day'] = df['Appointment_day'].dt.date 
                                     

# confirm changes
df.head()

	Gender	Scheduled_day	Appointment_day	Age	Neighbourhood	Scholarship	Hypertension	Diabetes	Alcoholism	SMS_received	No_show
0	F	2016-04-29	2016-04-29	62	JARDIM DA PENHA	No	Yes	No	No	No	Showed_up
1	M	2016-04-29	2016-04-29	56	JARDIM DA PENHA	No	No	No	No	No	Showed_up
2	F	2016-04-29	2016-04-29	62	MATA DA PRAIA	No	No	No	No	No	Showed_up
3	F	2016-04-29	2016-04-29	8	PONTAL DE CAMBURI	No	No	No	No	No	Showed_up
4	F	2016-04-29	2016-04-29	56	JARDIM DA PENHA	No	Yes	Yes	No	No	Showed_up

# checking the shape of the cleaned dataset
df.shape

(106987, 11)

Exploratory Data Analysis

Research Question 1 Is there any relationship between a patients gender and no-show ?

# checking the number of female and male patients
df['Gender'].value_counts()

F    70118
M    36869
Name: Gender, dtype: int64

We now create a new data set consisting of patients who don't show up for appointments.

# get the patients who show up for appointments and those who did not in two separate data frames
df_show=df.query('No_show=="Showed_up"')
df_noshow=df.query('No_show=="No_show_up"')

# checking the total number of females in the entire dataset
f_total=df.query('Gender =="F"').count()['Age']

# checking the total number of males in the entire dataset
m_total=df.query('Gender =="M"').count()['Age']

# we define a function to group any feature by no_show 

def stat(y):
    fea_stat=df_noshow.groupby([y],as_index=False)["No_show"].count()
    return pd.DataFrame(fea_stat)

# grouping the no show data frame by Gender and No_show using the function defined above.
stat('Gender')

	Gender	No_show
0	F	14275
1	M	7405

# we find the proportions of females and males who do not show up for appointments

fprop_noshow=(14275/f_total)*100  # proportion of females who do not show up for appointments
mprop_noshow=(7405/m_total)*100 #proportion of males who do not show up for appointments
print('The proportion of female patients is',fprop_noshow, "and",mprop_noshow,"for male patients who do not show up.")

The proportion of female patients is 20.358538463732565 and 20.084623938810385 for male patients who do not show up.

From the calculation of the proportions above, we can observe that females are more likely to miss appointments than males with a very small margin.

# plotting the bar chart for Gender and No_show appointments.
locations=[1,2]
labels=['Female','Male']
heights=[fprop_noshow,mprop_noshow]
plt.bar(locations,heights,tick_label=labels, color=['red','purple'])
plt.title('Gender vs No_show',fontsize=14)
plt.xlabel("Gender",fontsize=14)
plt.ylabel("Percentage of No_show", fontsize=14);
stat('Gender')

	Gender	No_show
0	F	14275
1	M	7405

From the bar chart above we can conclude that females are more likely to miss appointments than males though by a very small margin.

# checking the correlation between gender and No_show.
df['No_show']=df['No_show'].astype('category').cat.codes
df['Gender']=df['Gender'].astype('category').cat.codes
df.corr()

	Gender	Age	No_show
Gender	1.000000	-0.094108	0.003238
Age	-0.094108	1.000000	0.067025
No_show	0.003238	0.067025	1.000000

We also see that there is a very weak positive correlation between the gender of the patients and No_show

Research Question 2 Do patients on Scholarship show up for appointments more than those who are not on Scholarship?

# Checking the total number of patients with Scholarships.
total_01= df.query('Scholarship == "Yes"').count()['Age']

# Checking the total number of patients with no Scholarships.
total_02=df.query('Scholarship == "No"').count()['Age']

# we group the no show dataframe by scholarship and No_show using the function we defined earlier.
stat('Scholarship')

	Scholarship	No_show
0	No	19109
1	Yes	2571

# finding the proportions of patients who did not show up for appointment and whether or not they had scholarships.
with_sch=(2571/total_01)*100
withno_sch=( 19109/total_02)*100
print('The proportion of patients with scholarships is',with_sch, "and",withno_sch,"with no scholarships")

The proportion of patients with scholarships is 23.7857341104635 and 19.8683690656907 with no scholarships

# plotting the bar chart for proportion of patients who did not show up for appointment and whether 
# they had scholarships or not.
locations = [1,2]
label=['Scholarship','No_scholarship']
heights=[with_sch,withno_sch]
plt.bar(locations, heights,tick_label=label,color=['pink','grey'])
plt.xlabel('Scholarship',fontsize=14)
plt.ylabel('percentage of No_show',fontsize=14)
plt.title('Scholarship vs No_show',fontsize=14)
stat('Scholarship')

	Scholarship	No_show
0	No	19109
1	Yes	2571

From the bar chart above, we observe that the proportion of patients who have no scholarships are less likely to miss appointments compared to those who have scholarships.

Research Question 3. Is there any relationship between a patients age and no-show ?

# Plotting the historam of the age distibution of no show appointments.
df_noshow['Age'].plot(kind='hist',figsize=(5,5),color= 'green');
plt.title('Age distribution of No-show Appointments',fontsize=14)
plt.xlabel('Patients Age',fontsize=14);
plt.ylabel('Count of Appointments',fontsize=14);
pd.DataFrame(df.Age.describe())

	Age
count	106987.000000
mean	38.316085
std	22.466214
min	1.000000
25%	19.000000
50%	38.000000
75%	56.000000
max	115.000000

From the histogram above, we see that the distribution of age is very much skewed to the right. This means that the majority of no show appointments are common in children and adults. Most of the elderly patients are more likely to show up for appointments.

Research Question 4. Does getting an SMS reminder have any impact on show up rate?

# checking the total number of patients who received an Sms.
total_001=df.query('SMS_received == "Yes"').count()['Age']

# checking the total number of patients who did not received an Sms.
total_002=df.query('SMS_received == "No"').count()['Age']

# grouping the No_show data frame by SMS_received and whether they showed up for an appointment or not 
# using the function stat.
stat('SMS_received')

	SMS_received	No_show
0	No	12112
1	Yes	9568

def chart(x):
    """This function groups a certain feature (x) by no-show and plots a pie chart representing the % of no-show
       appointments in relation to the given feature.
    """ 
    fea_stat=df_noshow.groupby([x],as_index=False)["No_show"].count()
    chart_plot=px.pie(fea_stat,names=x,values='No_show',color_discrete_sequence=px.colors.sequential.RdBu,width=700,height=600 )
    return chart_plot 

# plotting the pie chart to show the percentage of no show appointments in relation to Sms received using
# the function defined above

print('Proportion of no-show appointments in relation to SMS_received.')
chart('SMS_received')

Proportion of no-show appointments in relation to SMS_received.

From the pie chart above, we see that 44.1% of the patients who did not show up for appointment received Sms notifications and 55.9% of patients who did not receive the notification did not show up.

We now find the proportions of patients that did not show up for appointment and whether or not they received an Sms to remind them about the appointment.

# calulating the proportion of patients who received sms and those who did not 
no_sms=(12112/total_002)*100 #Proportion of those who did not show up for appointment and did not receive an sms 

sms=(9568/total_001)*100  #Proportion of those who did not show up for appointment but received an sms 
print('The proportion of patients who did not show up for appointment but received an sms is',sms, "and",no_sms, " for those who did not receive an sms")

The proportion of patients who did not show up for appointment but received an sms is 27.665172762758424 and 16.728819645866135  for those who did not receive an sms

# plotting the bar chart of the proportion of patients who received sms and those who did not
locations = [1,2]
label=['Received_sms','No_sms']
heights=[sms, no_sms]
plt.bar(locations, heights,tick_label=label,color=['blue','yellow'])
plt.xlabel('Sms', fontsize=14)
plt.ylabel('percentage of No_show', fontsize=14)
plt.title('Relationship between Sms and No_show', fontsize=14);
df_noshow.groupby(['SMS_received'])[['No_show']].count()

	No_show
SMS_received
No	12112
Yes	9568

From the bar chart above, we see that the majority of patients that received Sms did not show up for appointments

Research Question 5 Which neighbourhoods have patients who don't show up for appointments?

In order to answer the above research question, we will make use of the pie chart. We are going to investigate the top 5 neighbourhoods that do not show up for appointments.

# In the data from for No_show, we check the value counts of neighbourhoods that dont show up for appointments
# then plot the pie .

df_noshow['Neighbourhood'].value_counts()[:5].plot(kind='pie',figsize=(10,10),autopct='%0.1F%%');
plt.title('Neighbourhoods with least number of appointments',fontsize=20);
pd.DataFrame(df_noshow['Neighbourhood'].value_counts()[:5])

	Neighbourhood
JARDIM CAMBURI	1432
MARIA ORTIZ	1194
ITARARÉ	898
RESISTÊNCIA	875
CENTRO	692

• JARDIM CAMBURI
• MARIA ORTIZ
• ITARARE
• RESISTENCIA
• CENTRO

These are among the top five Neighbourhoods that don't show up for appointments

Research Question 6. Does the kind of condition or disease the patient has play a role in determining whether a patient will show up for an appointment ?

# Checking the count of patients with Hypertension who did not show up for appointments
stat('Hypertension')

	Hypertension	No_show
0	No	17908
1	Yes	3772

# plotting the pie chart to show the percentage of no show appointments in relation to Hypertension using
# the function chart defined earlier

print('Proportion of no-show appointments in relation to Hypertension.')
chart('Hypertension')

Proportion of no-show appointments in relation to Hypertension.

From the pie chart above, we see that 17.4% of the patients who did not show up for appointment had hypertension and 82.6% of patients who had no hypertension did not show up. The majority of patients who did not show up for appointments had no Hypertension.

# Checking the summary statistics of patients with Diabetes who did not show up for appointments.
df_noshow.Diabetes.describe()

count     21680
unique        2
top          No
freq      20250
Name: Diabetes, dtype: object

# plotting the pie chart to show the percentage of no show appointments in relation to Diabetes using
# the function defined earlier

print('Proportion of no-show appointments in relation to Diabetes.')
chart('Diabetes')

Proportion of no-show appointments in relation to Diabetes.

From the pie chart above, we see that 6.6% of the patients who did not show up for appointment had Diabetes and 93.4% of patients who had no Diabetes did not show up. The majority of patients who did not show up for appointments had no Diabetes

# Checking the count of patients with Alcoholism who did not show up for appointments
df_noshow.Alcoholism.describe()

count     21680
unique        2
top          No
freq      21003
Name: Alcoholism, dtype: object

# Checking the count of patients with Alcoholism who did not show up for appointments
stat('Alcoholism')

	Alcoholism	No_show
0	No	21003
1	Yes	677

# plotting the pie chart to show the percentage of no show appointments in relation to Alcoholism using
# the function chart

print('Proportion of no-show appointments in relation to Alcoholism.')
chart('Alcoholism')

Proportion of no-show appointments in relation to Alcoholism.

From the pie chart above, we see that 3.12% of the patients who did not show up for appointment had Alcoholism and 96.9% of patients who had no Alcoholism did not show up. The majority of patients who did not show up for appointments had no Alcoholism problem.

We now group the No_show column by Hypertension, Alcoholism, and Diabetes, in order to check if there is any relationship between the No_show and the patients medical condition.

#plotting the grouped bar chart
df_noshow.groupby(['Hypertension','Alcoholism','Diabetes'])['No_show'].value_counts().unstack().plot(kind="barh",figsize=(10,10),color='violet');
plt.title ('Groped bar Chart for Patients medical conditions', fontsize=14)
plt.xlabel('Counts of No-show appointments', fontsize=14)
plt.ylabel( 'Patients with Hypertension,Alcoholism and Diabetes',fontsize=14);

From the above analysis, we observe that patients who have no Hypertension, Diabetes and who are not Alcoholic are more likely to miss appointments.

Conclusions

In this project, we investigated the No-show appointment dataset for Medical appointments in Brazil for the year 2016. Upon loading the data and checking for cleanliness, it was observed that the dataset contained no duplicates and no missing or null values. The only cleaning that was done was to drop some extraneous columns which were not relevant for our analysis such as patientID, Handcap and AppointmentID. One of the limitation for the Handcap column was that it contained numerical values 0, 1, 2,3 and 4 which were difficult to encode properly and as a result, this column had to be dropped. The other limitation was that the dataset contained a negative value for age and all those rows with age has $0$ were dropped. The limitation could be that there maybe there could be other better ways of dealing with such cases and also, the dataset set contained more object data types than numeric ones.

In the data cleaning process, we also made a few changes to the column names by putting underscores to ScheduledDay and AppointmentDay and changing No-show to No_show. We also made changes to the format for Scheduled_day and Appointment_day to date-time.

From the exploratory data analysis, we endeavored to answer the questions that was posed in order to determine whether or not patients show up for appointments and also find out what factors are important to know in order to predict if a patient will show up for appointment or not.

Upon undertaking the exploratory data analysis, it was observed that females are more likely to miss appointments than males though with a small margin. It was also observed that patients who have no scholarships are not likely to miss appointments as compared to those who have. We further observed that the majority of no show appointments were common in children and aldults as opposed to the eldery. In conclusion, we also observed that the majority of patients that recieved Sms did not show up for appointments.

References

https://www.youtube.com/watch?v=O4538i9MQEc

https://stackoverflow.com/questions/51241575/calculate-correlation-between-columns-of-strings

https://www.tutorialsandyou.com/matplotlib/how-to-show-percentage-and-value-in-matplotlib-pie-chart-12.html

https://www.projectpro.io/recipes/convert-string-datetime-in-python

https://www.kaggle.com/joniarroba/noshowappointments

https://www.w3schools.com/python/python_functions.asp